OpenRaiser/Intern-Atlas huggingface dataset:
Discrete symbolic optimization and Boltzmann sampling by continuous neural dynamics: Gradient Symbolic Computation
Venue: arXiv cs.CL 2018
Year: 2018

Gradient Symbolic Computation is proposed as a means of solving discrete global optimization problems using a neurally plausible continuous stochastic dynamical system. Gradient symbolic dynamics involves two free parameters that must be adjusted as a function of time to obtain the global maximizer at the end of the computation. We provide a summary of what is known about the GSC dynamics for special cases of settings of the parameters, and also establish that there is a schedule for the two parameters for which convergence to the correct answer occurs with high probability. These results put the empirical results already obtained for GSC on a sound theoretical footing.
Unsupervised Part-of-Speech Induction
Venue: arXiv cs.CL 2018
Year: 2018

Part-of-Speech (POS) tagging is an old and fundamental task in natural language processing. While supervised POS taggers have shown promising accuracy, it is not always feasible to use supervised methods due to lack of labeled data. In this project, we attempt to unsurprisingly induce POS tags by iteratively looking for a recurring pattern of words through a hierarchical agglomerative clustering process. Our approach shows promising results when compared to the tagging results of the state-of-the-art unsupervised POS taggers.
Improved pseudorandom generators from pseudorandom multi-switching lemmas
Venue: arXiv cs.CC 2018
Year: 2018

We give the best known pseudorandom generators for two touchstone classes in unconditional derandomization: an $\varepsilon$-PRG for the class of size-$M$ depth-$d$ $\mathsf{AC}^0$ circuits with seed length $\log(M)^{d+O(1)}\cdot \log(1/\varepsilon)$, and an $\varepsilon$-PRG for the class of $S$-sparse $\mathbb{F}_2$ polynomials with seed length $2^{O(\sqrt{\log S})}\cdot \log(1/\varepsilon)$. These results bring the state of the art for unconditional derandomization of these classes into sharp alignment with the state of the art for computational hardness for all parameter settings: improving on the seed lengths of either PRG would require breakthrough progress on longstanding and notorious circuit lower bounds. The key enabling ingredient in our approach is a new \emph{pseudorandom multi-switching lemma}. We derandomize recently-developed \emph{multi}-switching lemmas, which are powerful generalizations of Håstad's switching lemma that deal with \emph{families} of depth-two circuits. Our pseudorandom multi-switching lemma---a randomness-efficient algorithm for sampling restrictions that simultaneously simplify all circuits in a family---achieves the parameters obtained by the (full randomness) multi-switching lemmas of Impagliazzo, Matthews, and Paturi [IMP12] and Håstad [Hås14]. This optimality of our derandomization translates into the optimality (given current circuit lower bounds) of our PRGs for $\mathsf{AC}^0$ and sparse $\mathbb{F}_2$ polynomials.
Finite Blocklength and Dispersion Bounds for the Arbitrarily-Varying Channel
Venue: arXiv cs.IT 2018
Year: 2018

Finite blocklength and second-order (dispersion) results are presented for the arbitrarily-varying channel (AVC), a classical model wherein an adversary can transmit arbitrary signals into the channel. A novel finite blocklength achievability bound is presented, roughly analogous to the random coding union bound for non-adversarial channels. This finite blocklength bound, along with a known converse bound, is used to derive bounds on the dispersion of discrete memoryless AVCs without shared randomness, and with cost constraints on the input and the state. These bounds are tight for many channels of interest, including the binary symmetric AVC. However, the bounds are not tight if the deterministic and random code capacities differ.
Topic-based Evaluation for Conversational Bots
Venue: arXiv cs.CL 2018
Year: 2018

Dialog evaluation is a challenging problem, especially for non task-oriented dialogs where conversational success is not well-defined. We propose to evaluate dialog quality using topic-based metrics that describe the ability of a conversational bot to sustain coherent and engaging conversations on a topic, and the diversity of topics that a bot can handle. To detect conversation topics per utterance, we adopt Deep Average Networks (DAN) and train a topic classifier on a variety of question and query data categorized into multiple topics. We propose a novel extension to DAN by adding a topic-word attention table that allows the system to jointly capture topic keywords in an utterance and perform topic classification. We compare our proposed topic based metrics with the ratings provided by users and show that our metrics both correlate with and complement human judgment. Our analysis is performed on tens of thousands of real human-bot dialogs from the Alexa Prize competition and highlights user expectations for conversational bots.
Applying Vector Space Model (VSM) Techniques in Information Retrieval for Arabic Language
Venue: arXiv cs.IR 2018
Year: 2018

Information Retrieval (IR) allows the storage, management, processing and retrieval of information, documents, websites, etc. Building an IR system for any language is imperative. This is evident through the massive conducted efforts to build IR systems using any of its models that are valid for certain languages. This report presents an implementation for a core IR technique which is Vector Space Model (VSM). We have chosen VSM model for our project since it is a term weighting scheme, and the retrieved documents could be sorted according to their relevancy degree. One other significant feature for such technique is the ability to get a relevance feedback from the users of the system; users can judge whether the retrieved document is relative to their need or not. The developed system has been validated through building an Arabic IR website using server side scripting. The experiments verifies the effectiveness of our system to apply all techniques of vector space model and valid over Arabic language.
Open source platform Digital Personal Assistant
Venue: arXiv cs.HC 2018
Year: 2018

Nowadays Digital Personal Assistants (DPA) become more and more popular. DPAs help to increase quality of life especially for elderly or disabled people. In this paper we develop an open source DPA and smart home system as a 3-rd party extension to show the functionality of the assistant. The system is designed to use the DPA as a learning platform for engineers to provide them with the opportunity to create and test their own hypothesis. The DPA is able to recognize users' commands in natural language and transform it to the set of machine commands that can be used to control different 3rd-party application. We use smart home system as an example of such 3rd-party. We demonstrate that the system is able to control home appliances, like lights, or to display information about the current state of the home, like temperature, through a dialogue between a user and the Digital Personal Assistant.
Kinematics and dynamics of an egg-shaped robot with a gyro driven inertia actuator
Venue: arXiv cs.RO 2018
Year: 2018

The manuscript discusses still preliminary considerations with regard to the dynamics and kinematics of an egg shaped robot with an gyro driven inertia actuator. The method of calculation follows the idea that we would like to express the entire dynamic equations in terms of moments instead of forces. Also we avoid to derive the equations from a Lagrange function with constraints. The result of the calculations is meant to be applicable to two robot prototypes that have been build at the AES\&R Laboratory at the National Chung Cheng University in Taiwan.
Can Negligible Cooperation Increase Network Capacity? The Average-Error Case
Venue: arXiv cs.IT 2018
Year: 2018

In communication networks, cooperative strategies are coding schemes where network nodes work together to improve network performance metrics such as sum-rate. This work studies encoder cooperation in the setting of a discrete multiple access channel with two encoders and a single decoder. A node in the network that is connected to both encoders via rate-limited links, referred to as the cooperation facilitator (CF), enables the cooperation strategy. Previously, the authors presented a class of multiple access channels where the average-error sum-capacity has an infinite derivative in the limit where CF output link capacities approach zero. The authors also demonstrated that for some channels, the maximal-error sum-capacity is not continuous at the point where the output link capacities of the CF equal zero. This work shows that the the average-error sum-capacity is continuous when CF output link capacities converge to zero; that is, the infinite derivative of the average-error sum-capacity is not a result of its discontinuity as in the maximal-error case.
Graphene-Based terahertz antennas for area-constrained applications
Venue: arXiv cs.ET 2018
Year: 2018

Terahertz technology has made significant advances in the fields of spectroscopy, imaging and, more recently, wireless communications. In the latter, the use of this frequency band between 0.1 and 10 THz becomes extremely attractive due to the abundance of bandwidth and the potential for low area and power footprints, yet challenging given the large propagation losses and the lack of mature devices and circuits for terahertz operation. Maturity issues aside, this combination of features renders terahertz wireless communications desirable for highly integrated applications where area may be a decisive metric.
On a Stackelberg Subset Sum Game
Venue: arXiv cs.DM 2018
Year: 2018

This contribution deals with a two-level discrete decision problem, a so-called Stackelberg strategic game: A Subset Sum setting is addressed with a set $N$ of items with given integer weights. One distinguished player, the leader, may alter the weights of the items in a given subset $L\subset N$, and a second player, the follower, selects a solution $A\subseteq N$ in order to utilize a bounded resource in the best possible way. Finally, the leader receives a payoff from those items of its subset $L$ that were included in the overall solution $A$, chosen by the follower. We assume that the follower applies a publicly known, simple, heuristic algorithm to determine its solution set, which avoids having to solve NP-hard problems. Two variants of the problem are considered, depending on whether the leader is able to control (i.e., change) the weights of its items (i) in the objective function or (ii) in the bounded resource constraint. The leader's objective is the maximization of the overall weight reduction, for the first variant, or the maximization of the weight increase for the latter one. In both variants there is a trade-off for each item between the contribution value to the leader's objective and the chance of being included in the follower's solution set. We analyze the leader's pricing problem for a natural greedy strategy of the follower and discuss the complexity of the corresponding problems. We show that setting the optimal weight values for the leader is, in general, NP-hard. It is even NP-hard to provide a solution within a constant factor of the best possible solution. Exact algorithms, based on dynamic programming and running in pseudopolynomial time, are provided. The additional cases, in which the follower faces a continuous (linear relaxation) version of the above problems, are shown to be straightforward to solve.
Multi-Band Covariance Interpolation with Applications in Massive MIMO
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we study the problem of multi-band (frequency-variant) covariance interpolation with a particular emphasis towards massive MIMO applications. In a massive MIMO system, the communication between each BS with $M \gg 1$ antennas and each single-antenna user occurs through a collection of scatterers in the environment, where the channel vector of each user at BS antennas consists in a weighted linear combination of the array responses of the scatterers, where each scatterer has its own angle of arrival (AoA) and complex channel gain. The array response at a given AoA depends on the wavelength of the incoming planar wave and is naturally frequency dependent. This results in a frequency-dependent distortion where the second order statistics, i.e., the covariance matrix, of the channel vectors varies with frequency. In this paper, we show that although this effect is generally negligible for a small number of antennas $M$, it results in a considerable distortion of the covariance matrix and especially its dominant signal subspace in the massive MIMO regime where $M \to \infty$, and can generally incur a serious degradation of the performance especially in frequency division duplexing (FDD) massive MIMO systems where the uplink (UL) and the downlink (DL) communication occur over different frequency bands. We propose a novel UL-DL covariance interpolation technique that is able to recover the covariance matrix in the DL from an estimate of the covariance matrix in the UL under a mild reciprocity condition on the angular power spread function (PSF) of the users. We analyze the performance of our proposed scheme mathematically and prove its robustness under a sufficiently large spatial oversampling of the array. We also propose several simple off-the-shelf algorithms for UL-DL covariance interpolation and evaluate their performance via numerical simulations.
Breaking Mignotte's Sequence Based Secret Sharing Scheme Using SMT Solver
Venue: arXiv cs.CR 2018
Year: 2018

The secret sharing schemes are the important tools in cryptography that are used as building blocks in many secured protocols. It is a method used for distributing a secret among the participants in a manner that only the threshold number of participants together can recover the secret and the remaining set of participants cannot get any information about the secret. Secret sharing schemes are absolute for storing highly sensitive and important information. In a secret sharing scheme, a secret is divided into several shares. These shares are then distributed to the participants one each and thus only the threshold (t) number of participants can recover the secret. In this paper we have used Mignotte's Sequence based Secret Sharing for distribution of shares to the participants. A (k, m) Mignotte's sequence is a sequence of pair wise co-prime positive integers. We have proposed a new method for reconstruction of secret even with t-1 shares using the SMT solver.
To Pool or Not To Pool? Revisiting an Old Pattern
Venue: arXiv cs.SE 2018
Year: 2018

We revisit the well-known object-pool design pattern in Java. In the last decade, the pattern has attracted a lot of criticism regarding its validity when used for light-weight objects that are only meant to hold memory rather than any other resources (database connections, sockets etc.) and in fact, common opinion holds that is an anti-pattern in such cases. Nevertheless, we show through several experiments in different systems that the use of this pattern for extremely short-lived and light-weight memory objects can in fact significantly reduce the response time of high-performance multi-threaded applications, especially in memory-constrained environments. In certain multi-threaded applications where high performance is a requirement and/or memory constraints exist, we recommend therefore that the object pool pattern be given consideration and tested for possible run-time as well as memory footprint improvements.
Towards dynamic interaction-based model
Venue: arXiv cs.SI 2018
Year: 2018

In this paper, we investigate how dynamic properties of reputation can influence the quality of users ranking. Reputation systems should be based on rules that can guarantee a high level of trust and help identifying unreliable units. To understand the effectiveness of dynamic properties in the evaluation of reputation, we propose our own model (DIB-RM) that is based on three factors: forgetting, cumulative, and activity period. In order to evaluate the model, we use data from StackOverflow, which also has its own reputation model. We estimate similarity of ratings between DIB-RM and the StackOverflow model so to check our hypothesis. We use two values to calculate our metric: DIB-RM reputation and $historical$ reputation. We found that $historical$ reputation gives better metric values. Our preliminary results are presented for different sets of values of the aforementioned factors in order to analyze how effectively the model can be used for modeling reputation systems.
Inner Bound for the Capacity Region of Noisy Channels with an Authentication Requirement
Venue: arXiv cs.IT 2018
Year: 2018

The rate regions of many variations of the standard and wire-tap channels have been thoroughly explored. Secrecy capacity characterizes the loss of rate required to ensure that the adversary gains no information about the transmissions. Authentication does not have a standard metric, despite being an important counterpart to secrecy. While some results have taken an information-theoretic approach to the problem of authentication coding, the full rate region and accompanying trade-offs have yet to be characterized. In this paper, we provide an inner bound of achievable rates with an average authentication and reliability constraint. The bound is established by combining and analyzing two existing authentication schemes for both noisy and noiseless channels. We find that our coding scheme improves upon existing schemes.
The efficiency of community detection by most similar node pairs
Venue: arXiv cs.DL 2018
Year: 2018

Community analysis is an important way to ascertain whether or not a complex system consists of sub-structures with different properties. In this paper, we give a two level community structure analysis for the SSCI journal system by most similar co-citation pattern. Five different strategies for the selection of most similar node (journal) pairs are introduced. The efficiency is checked by the normalized mutual information technique. Statistical properties and comparisons of the community results show that both of the two level detection could give instructional information for the community structure of complex systems. Further comparisons of the five strategies indicates that, the most efficient strategy is to assign nodes with maximum similarity into the same community whether the similarity information is complete or not, while random selection generates small world local community with no inside order. These results give valuable indication for efficient community detection by most similar node pairs.
Resolvability on Continuous Alphabets
Venue: arXiv cs.IT 2018
Year: 2018

We characterize the resolvability region for a large class of point-to-point channels with continuous alphabets. In our direct result, we prove not only the existence of good resolvability codebooks, but adapt an approach based on the Chernoff-Hoeffding bound to the continuous case showing that the probability of drawing an unsuitable codebook is doubly exponentially small. For the converse part, we show that our previous elementary result carries over to the continuous case easily under some mild continuity assumption.
Multi-Task Spatiotemporal Neural Networks for Structured Surface Reconstruction
Venue: arXiv cs.CV 2018
Year: 2018

Deep learning methods have surpassed the performance of traditional techniques on a wide range of problems in computer vision, but nearly all of this work has studied consumer photos, where precisely correct output is often not critical. It is less clear how well these techniques may apply on structured prediction problems where fine-grained output with high precision is required, such as in scientific imaging domains. Here we consider the problem of segmenting echogram radar data collected from the polar ice sheets, which is challenging because segmentation boundaries are often very weak and there is a high degree of noise. We propose a multi-task spatiotemporal neural network that combines 3D ConvNets and Recurrent Neural Networks (RNNs) to estimate ice surface boundaries from sequences of tomographic radar images. We show that our model outperforms the state-of-the-art on this problem by (1) avoiding the need for hand-tuned parameters, (2) extracting multiple surfaces (ice-air and ice-bed) simultaneously, (3) requiring less non-visual metadata, and (4) being about 6 times faster.
Brain Age Prediction Based on Resting-State Functional Connectivity Patterns Using Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Brain age prediction based on neuroimaging data could help characterize both the typical brain development and neuropsychiatric disorders. Pattern recognition models built upon functional connectivity (FC) measures derived from resting state fMRI (rsfMRI) data have been successfully used to predict the brain age. However, most existing studies focus on coarse-grained FC measures between brain regions or intrinsic connectivity networks (ICNs), which may sacrifice fine-grained FC information of the rsfMRI data. Whole brain voxel-wise FC measures could provide fine-grained FC information of the brain and may improve the prediction performance. In this study, we develop a deep learning method to use convolutional neural networks (CNNs) to learn informative features from the fine-grained whole brain FC measures for the brain age prediction. Experimental results on a large dataset of resting-state fMRI demonstrate that the deep learning model with fine-grained FC measures could better predict the brain age.
A Hardware-Friendly Algorithm for Scalable Training and Deployment of Dimensionality Reduction Models on FPGA
Venue: arXiv cs.LG 2018
Year: 2018

With ever-increasing application of machine learning models in various domains such as image classification, speech recognition and synthesis, and health care, designing efficient hardware for these models has gained a lot of popularity. While the majority of researches in this area focus on efficient deployment of machine learning models (a.k.a inference), this work concentrates on challenges of training these models in hardware. In particular, this paper presents a high-performance, scalable, reconfigurable solution for both training and deployment of different dimensionality reduction models in hardware by introducing a hardware-friendly algorithm. Compared to state-of-the-art implementations, our proposed algorithm and its hardware realization decrease resource consumption by 50\% without any degradation in accuracy.
Cognitive Non-Orthogonal Multiple Access with Cooperative Relaying: A New Wireless Frontier for 5G Spectrum Sharing
Venue: arXiv cs.IT 2018
Year: 2018

Two emerging technologies towards 5G wireless networks, namely non-orthogonal multiple access (NOMA) and cognitive radio (CR), will provide more efficient utilization of wireless spectrum in the future. In this article, we investigate the integration of NOMA with CR into a holistic system, namely cognitive NOMA network, for more intelligent spectrum sharing. Design principles of cognitive NOMA networks are perfectly aligned to functionality requirements of 5G wireless networks, such as high spectrum efficiency, massive connectivity, low latency, and better fairness. Three different cognitive NOMA architectures are presented, including underlay NOMA networks, overlay NOMA networks, and CR-inspired NOMA networks. To address inter- and intra-network interference which largely degrade the performance of cognitive NOMA networks, cooperative relaying strategies are proposed. For each cognitive NOMA architecture, our proposed cooperative relaying strategy shows its potential to significantly lower outage probabilities. Furthermore, we discuss open challenges and future research directions on implementation of cognitive NOMA networks.